{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlib 画曼德博集\n",
    "\n",
    "- NumPy 是 Python 语言的一个扩展程序库，支持大量的维度数组与矩阵运算，此外也针对数组运算提供大量的数学函数库。\n",
    "- Matplotlib 是 Python 的绘图库。 它可与 NumPy 一起使用\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[20 20 20 ... 20 20 20]\n [20 20 20 ... 20 20 20]\n [20 20 20 ... 20 20 20]\n ...\n [20 20 20 ... 20 20 20]\n [20 20 20 ... 20 20 20]\n [20 20 20 ... 20 20 20]]\n"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "y,x = np.ogrid[-1.4:1.4:400*1j,-2:0.8:400*1j]\n",
    "c = x+y*1j\n",
    "z = c \n",
    "# print(z.shape)   #返回数组的维度\n",
    "divtime=20+np.zeros(z.shape,dtype=int)  #返回给定形状和类型的新数组，并用零填充。\n",
    "print(divtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[0]\n [1]\n [2]\n [3]\n [4]\n [5]\n [6]\n [7]\n [8]\n [9]]\n[[0 2 4 6 8]]\n"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# ogrid用切片作为下标，返回的是一组可用来广播计算的数组。其切片下标有如下形式\n",
    "# 不设置步长默认为一  [ 开始值：结束值：步长 ]\n",
    "x,y = np.ogrid[0:10,0:10:2]\n",
    "# x,y=np.ogrid[1:4:3j,1:5:2j]\n",
    "# x,y=np.ogrid[-2:2:20j,-2:2:20j]\n",
    "# y,x=np.ogrid[-1.4:1.4:400*1j,-2:0.8:400*1j]\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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ULLJ7BiuIZ6RCa2YO4dF/Hh9iGsG+7PyPA4s9RBWMC2ZmJI3icT9KAunP69tPmPWowoockrtHJwGytBYEA6cfjq1ivgZ2vxz/e+ipentwMQQtvzMw1wsvG842urmY7xHoSCjbJJ1cWkbC2aHvdQWt5uBoU2zokE4jjTdYiuMfp8w5hKcPjpTa+gaztPZ5LZZLPrVDfmZxrgziEwsVUjJTNWkDPqWKrWTKvI5FqV6W1hlotCREZcRm/QhY7xnozMhlYnS///7VVvYqdvwpgTrjBuXfk3YVSUCZHGQvMfc0YdS01oWgQW99UW1NxF7TedVqEq9f0A0oGPDx9/RtMxbTIhqVpMpIZOrQHUj/U1o06tpIWm5VtEasFIjw0ge7PSanK1f1MaxG5Um7dqQxzW7xjvQezNlZpnaFsqtGKJrNBEX3Gfjfn1NYgEhMK+5R+44A6nwJ6NGXOiCqjtqQgoN84hmR/i+WQ2+tj05L6s/pEELQnGQGFCk70qrBaZUaUqUjdO+CZzIRKIo+lrU5j7VSu4Ne6i5rwR1AxMV2rPZqOO9pHdYJa6bQm3NKQI1K1BO4WE+YuSgkWSe7Uk+YrRki7lnU6BidTgfCT9eDUXuRjShWn6Tocw5FymZ6LW42X9TrQxnWBTcLJxONm4HWUsgHo2ilhzAh8+/kxGt60rq4XP99J90Yy+j2ZiudCK0TLgzjtOYJU30x1QKxRxoOKRL76Z0U2qELEpQdZnbc0X0NZ8wbC+99XIKm+EtrgjFdjELZ/oOoQf3vK6ZediyT6aUaXhemaDRVocGDl2IwDAWw8A2S2o5VxJqYji9cCL79yOM48/g1v271N1jHzHpL+XCYL8aO3hvMezyc03WsbRtyFdi8cHhI3+rrYg2kd2gx2rA4ukJeeSJTSj1mfFbNlGuLwe8G69iAdbT+LFd24HQPobSjc0lS0TEcGar0yhxRsSmr2IjpFPcPmsnnge2R++9u8g6VRGdAeudqSNiya6DtEq7I65FoTaa8GeE+5Tu3mtd51muEUrZuNRMTGWQd05B2LcSgxx2/Ctz/8Fv4zsgJNLL4xdYeKzC89xcekW1QTiKr6xYRgd4z3gvUnEWeEYrrBc33iVLqnMcIn+iYfLpkNVuULEt8obQWxmJVAsi6aHYvamB5SbjrrDKbjDQJz1ABvTnWv5pZB/dLYWTs6BtV/4FJ8cu3bp2WmxictSANAGN+0ju7Gm+QJOB1fDG0xHBuNsCldWywtW8dxFa77TwdXYhW5bbBbwybFrcc10yrK91Yx9NK1uY7EFRsjV3RcQ1mlxNkW7JK3yRnCg/SUAwIMf9GFr0zQAYOTYjTRD3inKlyO96Bv9C/hO+x8z2mZHljoriSueSY/BfGITiyzq58E0L9IWBbbraA69U5348283oe6cA+y5ZIbQ8nUwJuhxHauiwtoVFqwMP1uL+ZkGnA6uxo+n70Ork8X9rSdpZM/JxnH9ls/ANCv76mK//6c3vYJYcwIJNoWDdw9iYG4z2kd201y7nFNbZO6b6DqELg9vi8wEphJchsjUtBdXQo+B0eU6loolkyNXZoZnhkGcFdpqP9HyGgZD19M2bAdbRzFQexn7m46jI9KD+WbBJXSycfCcC042Ti0focvDo2vLRxibDOB34ZuEwQwXr8NpbvXSOg4Qu6JAtsDioq66Nuaw61Q3dfPrFLJEzEaT62jmxEu9IlNyG+n/JYWU5MLmA9Gc7bE7xntwf+tJnF1cTn8nt6c1MLeZPkZ8PynUZJoXM8bJZpy7qC3B9Vs+s9dmJkJ6O4ob9QDQ5ToStLiQqi1aOYpMiQSbAs+50NKm/EHd33oS65adz9t4VOl+cXPTwOyenOcCAJOz12BkLWO7jSaT3t5hMrZizK5Lo1dmLmtWiiKTonpAenMCjf4FAPIWilBoZ1/xhjPvTSKBzCBLQtL/4862oC0ykxiJMnCyccRZ4RqJ+lNC+QyX54kGkjcYUg4iU0Jus5iJ1GBr0zR+etMrpr62WDRdWz7C4N+/gOu3fEaDJwTem4STjdupVibS5eEx0XUI3q2XwAeiGPz7F/D+QLrmz4oqazofjfF4ZB/g8JiXcKl1fpgYuamYUpJuR9b6jHcDn8YbcCKxBjH3QsacLrNY7v4MXR4eH6eW4fjZa5Fyp+iN5OLN1SXxJe+c6edSzXzqZNBYH8U/XfM3bHpyHzwXUxnz0giyM9Pk0DBHLae/VcrRRYKSyMTWTLrxnOBcOM96l8Yemd+Xnli3/U3Hcdi/EfMzDQBARVbPRitiCEWpI/6M1SYUGIWs66i3fEUNRohMXNwpBxHZ5fXAQjuf1XTUycZxf+tJPLbirYLPRStHtg+h0b+ARv8C6tn0BrqN+QzMbaY/u8OFZ4Vo0YilXbDMFpnYikVaHHBxwEMPjGHo5DYkOBeNBPZOdeKxFW8Vrbvv/a0nactqAGU1HLAcGQz5sX/0AXiDLryKTjq+yUoc9zi+nhJHHEs1+CFek3FrlNd24jUZIKQ2/eFr/15yLbPJ/tua2stYt+x8Vqa5jbGIR+yKe8hoac4jh9q9tAzXsRRFJm4JF/M5cenGZYi0OLKy5mP12b8jLuNdr3+v5CqVH1vxFnb6JrBu2XkE3HYQxGyY5kUc/s6/AVBXO2j461vxInpElqvnIilNJ+LKLlVJQ/atSi183upkEXDPIeCeQ5trodinU/H0bHwvw6uxuiNahutohkXLJzI9Q/kudAhBjWVH6+h90nxCaQCkrfkCDrS/VHIupI113DC4F8tPQ9ecNCXUuo70KrZCZEY0GyX0bHwPL3K3wz1LHiOUwki71DLNi7Lzjm2qB5LnuHy6eP09TXMd87XkzkXM55RNDgaEtZhnhsGL79yOri0f0UwLIrJgz3NgmhfBe5PgvUL1bO9Upy2yKmVgbjMO3j0oBEEkazOr+u4DJoT3xS25taKl+ah71omxyQAttmwf2Q0Ggqjq2SiwtD91PuLFt1e9qflcbCqD/U3HBZexCKUxYpyAvNuotLZS8l8LERigrm22XNCjd6oTB1tH0bPxPZxdXI6DraOYauEwGW9Am2sBP56+D8FYE7o8dvi82th1qht//fPnhEJPTvBuitUWPuvqzhe80NPyLR9qLJmcyMRzwsTpNa1OFq1OHgArCC/BwR7WV318o2UcP+Nai1LoKSXjCtcjopjPiRjL4PJ6oOmI/P5Erm+RfCITF24Son7hdcKcB0fRgpFVueu47PVZ9UGs2fJp+SJPo1Dbfo5e5UoiyyUw+nO9AwmWB7e6Buy57PZdahJ/s+4TtcgWQ5qMdoz3YJU3Ylcl28hCXEaUwPxqYElohYgMEPatnJwgjFhYqFzN1ZWXIDXp0l6HcTYF79ZLdFQqINRvdYz34Mj2IYxEGTtsb5MBCeUXqzeIEk4gLShxuDPSmnnxErMrzTO8vB6INcfR6F/Ake1DSzllNRkbyO5wKqPtNUCEqZzRQcL1R7YPYd1MP2JeoT3A5Ow1tEmO4C7aIrMRCAztgXcpn5FQCpODAJHryK1xA0vD+4gwCIJ1ETr/ErFQCxOI4oxohpcvMI8QGuHeMY/I0RV0zhc/K4j5+i2f4Rst49g/+gD4QBwJzoVG/wLCnAcJzoWBzl8DAJ6euItWQR+8exCAICy7r4aNFNLlKtfs6mJTFX0dbWyKjeMex9dTiS9tA1CI65iQuI6MKtdRrgUbQZzpQWYSE9dxZ1uw5JKEbYqP9NoTt5UDsqOOSpkhWnIdAXX5jo57HF9PObbdJHun2mDI/PoaxNkUXJyDFtWZFQwRi3okyqDNtWAHQ2wo0v6NABTD+1YKzUkOLBd5dIUWZcXmDiUyxOYKCy28hB34pTelsAgVt/iSijFTeMLEltibKyFuG5Rga3Bk+5Ad3reRRTz/QDwLr9ik+zrmEBuQbd3It0PM54Q7nIKTc8juoYkfm/175T02NwfgXPaGtSvMIDAkNCQNN3vQEenBT296xQ6Q2GSw9guf4q9//hzcFjfhUSLjKlcSG6BsZl0hwAug8aS+FCypdcy6n+MBZIpN6KGfgjcQxdam6bwis/faqo83NgxjsMWPnx36B2B6aW/X5zQ8O0RXKwNAEFsuH5XcL70B+ssO1Lx5ORcgzKWdyoG5zbRdwVSCo5vZvVOd+G3kel3nZVPevDy9XZhZnWO/1iqcgKBKaQa/1gWh9PFarVsuy0aCK9L9PSDdomDopBA57QVwdK4FAOwymSrnjQ3DwAZhveYOm2fV1GD4PhqxcK7QIr2pxR1KqP4QYs0J7GwLYt1wPwJDe8AEPeBna3GwdRRhzkPnoIU5D35+fofet2NT5gzMbcbz33y+6FaN9gwph1YG4p4hj3zxTbz4TrqVgYuzWxnYyENC/l6ZVgZW9Qyxm/PYVA3FbM5jegpWvpMWu5hy7iZxJ92hhOBj1zuEviBBjzAyN5zdR52M0gWEDJQE58IbG4ZLTmQjUYYGbWzMZWBuMz7ue7ZoLqQluY5aAysAcq7vNj25j84hJjcliCtZag1UpxIcgrEmBGNNmIw3FPt0Kp6hk9syvtC09KcxgoxXk4s+GkWuPbpciDfM3aEEVoQSsi3BidgyNrc5YV71H+/9WclZsxcu3UFbggOwe5qYDD9bi/uf/i9wQViCWF0+wwBAcn6e/kLLXF4rEVs49mxM8LMlNwBZFu6RL76Ju8e+i3XD/dh1qhuAYN2K6a4NzG3Guxevw3TEh3cvXoexUHvRzqUaGAz5aetBadW+3j43WqHD4sth0AVBy9imBJvKGGfLNAti7dn4XtEmynSM92T8387ZtIaBuc20idNtP9gL9mwMQHZAxNROxXJPNkNwel1IMSTZWWmTm2xwLz8NxOoZGoV0cg4kUAvem8ThqY0ACp9VrZWO8Z6sQYQ21iD+W8fqHQVvXmvx/nIGQ8xyI/OleWlBMWFZ5IOLo5CAcIGv8kawbtl5Q84hHyNR4WMemNtMRQYIk0cBIZVMPCTPxhzEaXpyjZ/MpGwrrKVbA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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "def mandelbrot(h,w,maxit=20):\n",
    "    y,x = np.ogrid[-1.4:1.4:h*1j,-2:0.8:w*1j]\n",
    "    c = x+y*1j\n",
    "    z = c \n",
    "    divtime = maxit + np.zeros(z.shape,dtype=int)\n",
    "    for i in range(maxit):   \n",
    "        z = z**2 +c\n",
    "        diverge = z*np.conj(z) > 2**2\n",
    "        div_now = diverge & (divtime==maxit)\n",
    "        divtime[div_now] = i \n",
    "        z[diverge] = 2 \n",
    "    return divtime\n",
    " \n",
    "plt.imshow(mandelbrot(400,400))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}